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检索条件"任意字段=Conference on Next-Generation Communication and Sensor Networks 2007"

共 1567 条记录，以下是1-10 订阅

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Behavioural Aspects of 2.5 Ghz in an Indoor Environment 8th

Behavioural Aspects of 2.5 Ghz in an Indoor Environment

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8th International conference on Internet of Things and Connected Technologies, ICIoTCT 2023

作者： Mythili, S. Chaulagain, Aditi Kalamani, M. Prabhakaran, V. Department of Electronics and Communication Engineering Bannari Amman Institute of Technology Tamil Nadu Sathyamangalam India Department of Electronics and Communication Engineering KPR Institute of Engineering and Technology Tamil Nadu Sathyamangalam India Department of Civil Engineering Bannari Amman Institute of Technology Tamil Nadu Sathyamangalam India

ISBN: (纸本)9789819757855

In the current era, the world is propelled by the rapid advancements of next-generation technologies, which play a pivotal role in shaping various aspects of our lives. This research paper aims to delve into the intricate nature of indoor wireless communication channels operating at 2.5 GHz for wireless local area networks. The central objective of this study is to meticulously examine the prevailing elements that wield influence over signal transmission among sensor nodes strategically positioned within indoor environments. By comprehensively analysing these factors, we aim to gain a deeper understanding of the dynamics at play within indoor wireless networks. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： sensor nodes

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GCN-Integrated Multi-Agent Reinforcement Learning (MARL) with Modified Firefly Algorithm for Cross-Layer Optimization in Cognitive sensor networks 3

GCN-Integrated Multi-Agent Reinforcement Learning (MARL) wit...

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3rd International conference on Integrated Circuits and communication Systems, ICICACS 2025

作者： Jakranpally, Karthik Babu, Nagabathula Ramesh Babu, Doddavarapu Sreenu Rakesh, K. Barve, Amit Shelke, Atmaram Valiant It Services 4000 Stanwick ave SacramentoCA95835 United States J.B. Institute of Engineering & Technology Department of Electronics and Communication Engineering Hyderabad India Bharatratna Indira Gandhi College of Engineering Department of Electronics and Communication Engineering Solapur India Malla Reddy Engineering College for Women Department of Electronics and Communication Engineering Hyderabad India Parul University Parul Institute of Technology Faculty of Engineering and Technology Department of Computer Science and Engineering P.O.Limda Gujarat India Pune India

ISBN: (纸本)9798331508456

CSN s are integrating cognitive abilities, making it challenging to achieve optimized cross-layer performance with limited resources. Non-coordinated techniques typically demonstrate lower proficiency in dynamic spectrum management and network synchronization. We introduce Graph Convolutional networks (GCN) and Multi-Agent Reinforcement Learning (MARL) in our architectural solution, with the help of the specially designed Firefly Algorithm. The framework implements a distributed learning process, utilizing GCN for local topology perception and MARL for cooperative spectrum and optimal routing decisions. The results of experimental analysis show large gains compared to traditional methods. The proposed model provided a 94.2% recognition rate in spectrum hole identification, an 89.7% enhancement in network throughput, and a 78.5% diminution in end-to-end delay. The energy efficiency surpasses baseline techniques by 67.3%, while the raw packet delivery reliability remains at 92.8%. Integrating GCN with MARL and the improved Firefly Algorithm can significantly improve cross-layer optimization, offering a promising solution for the next generation of sensor networks. © 2025 IEEE.

关键词： Reinforcement learning

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Machine Learning Enabled Integrated Sensing and communication for Smart 6G Environments

Machine Learning Enabled Integrated Sensing and Communicatio...

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International conference on Intelligence in next generation networks (ICIN)

作者： Panagiotis Tirchas George T. Karetsos Dept. of Digital Systems University of Thessaly Larissa Greece

ISBN: (数字)9798331542399

ISBN: (纸本)9798331542405

Integrated sensing and communication (ISAC) inherently combines the functions of sensing and communication, leveraging wireless and hardware resources for the mutual benefit. ISAC is widely expected to play a pivotal role in next-generation wireless networks, supporting a broad spectrum of emerging applications by transmitting signals that serve both data communication and sensing purposes. This dual functionality can be applied in various contexts, such as environmental monitoring, smart cities, and industrial automation. In this work we present an experimental ISAC framework that serves as a proof of concept regarding the capabilities of this technology in future wireless networking environments. Furthermore, the acquired results corroborate that ISAC in combination with AI/ML techniques and in particular Convolutional Neural networks (CNNs) is an important enabler for creating smart 6G communication environments.

关键词： 6G mobile communication Wireless sensor networks Technological innovation Smart cities Wireless networks OFDM Machine learning Integrated sensing and communication Hardware next generation networking

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Sustainable Substrate Design for High-Performance Wi-Fi Antennas in Green Electronics 14

Sustainable Substrate Design for High-Performance Wi-Fi Ante...

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14th IEEE International conference on communication Systems and Network Technologies, CSNT 2025

作者： Mishra, Niketan Kumar Sen, Deepak Rajput, Rana Pratap Singh Rajput, Varun Depratment of Electrnics and Communication Engineering Lakshmi Narain College of Technology & Science Kalchuri Nagar Raisen Road Madhya Pradesh Bhopal462022 India Depratment of Electrnics and Communication Engineering Lakshmi Narain College of Technology Excellence Khajuri Khurd Raisen Road Madhya Pradesh Bhopal462022 India

ISBN: (纸本)9798331531935

Microstrip patch antennas are important parts of modern wireless communication systems because of their compact size, high efficiency, and versatility. In this paper, the effect of the properties of the substrate material on the performance of coaxial-feed rectangular microstrip patch antennas operating within the Wi-Fi band (2.4-2.485 GHz) is investigated. A comparative analysis was thus carried out on substrates such as FR-4 and RT Duroid 5870 using CST Studio Suite, evaluating gain, directivity, bandwidth, and return loss. This shows that low dielectric substrates, such as those of Rogers 5870, offer better performance since they provide optimal impedance matching, increased gain, and least return loss, thereby recommended for high-frequency applications. This study underlines the importance of substrate optimization in designing energy-efficient and green antennas for next-generation technologies such as 5G, IoT, and wireless sensor networks in the context of green electronics and sustainable communication systems. © 2025 IEEE.

关键词： Microstrip antennas

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SwarmCatcher: An AI-Powered Anti-Drone Surveillance System for Public Safety Over 5G and Beyond Ecosystems 28

SwarmCatcher: An AI-Powered Anti-Drone Surveillance System f...

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28th conference on Innovation in Clouds, Internet and networks, ICIN 2025

作者： Kalafatidis, Sarantis Tsiktsiris, Dimitrios Trakos, Dimitrios Lalas, Antonios Votis, Konstantinos Tzovaras, Dimitrios Centre for Research and Technology Hellas Information Technologies Institute Greece

ISBN: (纸本)9798331542399

The proliferation of unmanned aerial vehicles (UAVs) presents significant challenges to public safety and critical infrastructure protection. This paper introduces SwarmCatcher, an innovative AI-powered anti-drone and surveillance system designed to detect and classify UAV threats in real-time. SwarmCatcher integrates multi-sensor technologies, including optical, thermal, RF, and acoustic systems, with advanced AI algorithms to provide thorough situational awareness. Utilizing the low-latency, high-throughput capabilities of cloud-native architecture and 5G networks, the system supports scalable deployment across diverse environments, offering rapid threat mitigation. Real trials were conducted, utilizing 5G infrastructures in the FIDAL and CERTH testbeds, evaluating and demonstrating SwarmCatcher’s potential to address the requirements of public safety agencies and pave the way for integration with 6G networks. The paper investigates SwarmCatcher’s architecture, key performance metrics, and its role in advancing next-generation vertical-specific applications for public safety. © 2025 IEEE.

关键词： 5G mobile communication systems

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Resource Management for MEC-Enabled next-generation Wireless sensor networks

Resource Management for MEC-Enabled Next-Generation Wireless...

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Wireless Africa conference (WAC)

作者： B. S. Awoyemi M. C. Hlophe B. T. Maharaj Dept. of Electrical Electronic and Computer Engineering University of Pretoria Pretoria South Africa

ISBN: (数字)9798331517588

ISBN: (纸本)9798331517595

In emerging beyond 5G wireless network applications, such as next-generation wireless sensor networks (xWSNs), computationally demanding high-level algorithms are required to achieve their processing demands. By incorporating multi-access edge computing (MEC), a sizeable amount of communication and computational resources can be made available for such emerging networks. This paper develops an intelligent and efficient computation offloading scheme for MEC-enabled xWSNs. The work proposes a task offloading algorithm designed to reduce offloading costs by reasonably offloading sub-tasks to edge servers. Simulation results are presented which confirm that the proposed edge cooperation scheme has a significantly lower network cost than comparative schemes for the entire task-offloading and network processing.

关键词： Wireless sensor networks Energy consumption Costs Multi-access edge computing Simulation Wireless networks Computational efficiency Resource management Servers next generation networking

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SwarmCatcher: An AI-Powered Anti-Drone Surveillance System for Public Safety Over 5G and Beyond Ecosystems

SwarmCatcher: An AI-Powered Anti-Drone Surveillance System f...

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International conference on Intelligence in next generation networks (ICIN)

作者： Sarantis Kalafatidis Dimitrios Tsiktsiris Dimitrios Trakos Antonios Lalas Konstantinos Votis Dimitrios Tzovaras Centre for Research and Technology Hellas / Information Technologies Institute Greece

ISBN: (数字)9798331542399

ISBN: (纸本)9798331542405

The proliferation of unmanned aerial vehicles (UAVs) presents significant challenges to public safety and critical infrastructure protection. This paper introduces SwarmCatcher, an innovative AI-powered anti-drone and surveillance system designed to detect and classify UAV threats in real-time. Swarm-Catcher integrates multi-sensor technologies, including optical, thermal, RF, and acoustic systems, with advanced AI algorithms to provide thorough situational awareness. Utilizing the low-latency, high-throughput capabilities of cloud-native architecture and 5G networks, the system supports scalable deployment across diverse environments, offering rapid threat mitigation. Real trials were conducted, utilizing 5G infrastructures in the FIDAL and CERTH testbeds, evaluating and demonstrating SwarmCatcher's potential to address the requirements of public safety agencies and pave the way for integration with 6G networks. The paper investigates SwarmCatcher's architecture, key performance metrics, and its role in advancing next-generation vertical-specific applications for public safety.

关键词： Cloud computing Technological innovation 5G mobile communication Surveillance Prevention and mitigation Autonomous aerial vehicles Public security Real-time systems Critical infrastructure Systems support

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Cognitive Congestion Alleviation Framework in IoT-Enabled WSN for next-Gen Intelligent Transport Systems via Optimized Capsule Attention Network

Cognitive Congestion Alleviation Framework in IoT-Enabled WS...

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Intelligent Data communication Technologies and Internet of Things (IDCIoT), International conference on

作者： R. Mohandas Sudha Y Narmatha P P.Venkata Prasad N. Manikandan Department of Electronics & Communication Engineering Chennai Institute of Technology Chennai Tamil Nadu India Department of Computer Science and Engineering Presidency University Bangalore Karnataka India Department of Computer Science and Engineering Sri Sairam College of Engineering Bangalore Karnataka India Department of Electrical and Electronics Engineering Chaitanya Bharathi Institute of Technology Hyderabad Telangana India Department of Business Administration Kalasalingam Academy of Research and Education Kalasalingam Business School Krishnankoil Viruthunagar India

ISBN: (数字)9798331527549

ISBN: (纸本)9798331527556

The rapid advancement of Intelligent Transportation Systems (ITS) has heightened the importance of reliable, real-time data transmission in Wireless sensor networks (WSNs). However, congestion in data-heavy Internet of Things (IoT)-enabled networks remains a critical challenge, impacting communication efficiency and decision-making in ITS. In high-density IoT-ITS applications, traditional congestion control methods are insufficient to address the complexity and dynamics of data flows. Existing solutions often fail to adapt in real time to varying traffic loads, leading to delays and data loss. There is a strong need for a congestion alleviation framework capable of intelligently prioritizing data while dynamically managing network resources to ensure seamless information exchange in large-scale deployments. This study presents a Cognitive Congestion Alleviation Framework in IoT-Enabled WSN for next-Gen Intelligent Transport Systems via Optimized Capsule Attention Network (V-CapMiAN-Parr), which combines the Vectorized Adaptive Capsule Neural Network (V-AdCapNet) and Multi-instance Attention Network (MAN), fine-tuned by the Parrot Optimizer (ParrOpt). This integration aims to effectively detect, mitigate, and prevent congestion through an advanced capsule-based attention mechanism with adaptive optimization. The suggested V-CapMiAN-Parr framework demonstrated significant improvements in congestion control, achieving data throughput efficiencies above 99.7%, packet delivery rates above 99.5%, and network reliability reaching 99.2% under high traffic loads. The model's adaptive weighting mechanism ensures real-time responsiveness and reliability, crucial for next-gen ITS applications. The V-CapMiAN-Parr framework effectively addresses congestion issues in IoT-enabled WSNs, providing a robust, scalable solution for ITS applications. This cognitive framework's advanced data prioritization and adaptive optimization capabilities are well-suited for enhancing the performanc

关键词： Wireless communication Wireless sensor networks Adaptive systems Transportation Telecommunication traffic Real-time systems Internet of Things Data communication Optimization next generation networking

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Recent Advances in next generation Cellular Mobile networks-5G, 5G-Adv., and 6G

Recent Advances in Next Generation Cellular Mobile Networks-...

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Innovation in Computing and Engineering (ICE), International conference on

作者： Prem Chand Jain EE Dept. School of Engg. Shiv Nadar IoE Noida UP

ISBN: (数字)9798350380460

ISBN: (纸本)9798350380477

The evolution of cellular network standards has significantly transformed the way technology is perceived and utilized. Over the years, advancements in wireless communication have reshaped how people connect, communicate, and access information globally. The fifth-generation (5G) standard has introduced groundbreaking enhancements in wireless technology, including Enhanced Mobile Broadband (eMBB) supporting ultra-fast speeds and high-capacity data transfer, Massive Machine-Type communication (mMTC) enabling seamless connectivity for billions of IoT devices, and Ultra-Reliable Low Latency communication (uRLLC) providing near-instantaneous responsiveness with high reliability. With peak data rates of 20 Gbps and latency as low as 1ms, 5G leverages mmWave frequency spectrum to ensure high-speed, low-latency communication. It plays a critical role in enabling the Internet of Things (IoT) and is paving the way for the Internet of Everything (IoE) -a concept that envisions seamless connectivity between people, devices, and the smart environments. The sixth-generation (6G) network promises faster speeds, greater spectrum efficiency, and enhanced reliability. It aims to deliver 1 Terabit-per-second (Tbps) data rates and 100-microsecond (µs) latency to support Extended Reality (XR) applications, including Augmented Reality (AR) and Virtual Reality (VR). Beyond 90 GHz to 300 GHz spectrum is utilized to achieve ultra-high-speed wireless transmission in 6G. The AI-driven network intelligence allowing the creation of digital twin, a virtual replicas of physical entities transforming the network into an extension of human cognition, akin to a "sixth sense" brain network. The Non-Terrestrial networks (NTN) featuring Low Earth Orbit (LEO) satellites enhances connectivity for in-flight and onboard ship networks, as well as rural and remote areas. To achieve ubiquitous connectivity and seamless global coverage, 6G will integrate terrestrial, airborne, and satellite networks into a un

关键词： 6G mobile communication Wireless communication Wireless sensor networks Satellites 5G mobile communication Ecosystems Ultra reliable low latency communication Internet of Things Reliability Standards

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next-generation IoT Intrusion Detection: Leveraging Hybrid Deep Learning Algorithms for Enhanced Security

Next-Generation IoT Intrusion Detection: Leveraging Hybrid D...

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Computational Intelligence, communication Technology and Networking (CICTN), International conference on

作者： Urvashi Parul Agarwal Kamlesh Kumar Raghuvanshi Jawed Ahmed Department of Computer Science and Engineering Jamia Hamdard University Delhi India Department of Computer Science University of Delhi Delhi India

ISBN: (数字)9798331530389

ISBN: (纸本)9798331530396

Wireless sensor node and DDoS (Distributed Denial of Service) is a major attack way that can exhaust the energy of a sensor, disrupt the communication traffic of an IoT network. We must be on the lookout for these attacks. With the quantity of sensor nodes, traditional methods face difficulty in detecting network traffic analysis requiring big data analysis. This paper is dedicated to Network Traffic analysis using Big Data environment and detecting attacks using Deep Learning. Implemented with PySpark in Google Colab, using Keras and Scikit-Learn built-in libraries. To be more specific, training and testing are conducted on the ’CICIoT2023’ / ’TON IoT’ datasets, respectively. When we use a correlation feature reducer, it virtually considers the most significant features for inclusion and reduces all of our features. The first contains a hybrid deep leaning model combining one-dimensional EfficientDet and Gradient Boosting Machine (GBM), comparing it with ten other algorithms. It generated 99.95% binary classification accuracy for the CICIoT2023 dataset and 99.75% for TON IoT data.

关键词： Deep learning Wireless communication Wireless sensor networks Machine learning algorithms Intrusion detection Telecommunication traffic Big Data Classification algorithms Security Computer crime

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